Even though there are many variables in the dataframe, here is a quick description of some of the variables collected.
price= cost per nightproperty_type: type of accommodation (House, Apartment, etc.)room_type:- Entire home/apt (guests have entire place to themselves)
- Private room (Guests have private room to sleep, all other rooms shared)
- Shared room (Guests sleep in room shared with others)
number_of_reviews: Total number of reviews for the listingreview_scores_rating: Average review score (0 - 100)longitude,latitude: geographical coordinates to help us locate the listingneighbourhood*: three variables on a few major neighbourhoods in each city
listings1 <- listings %>%
group_by(property_type) %>%
summarize(count = n())
view(listings1)Exploratory Data Analysis
Looking at the raw values
listings %>%
glimpse()Rows: 6,296
Columns: 74
$ id <dbl> 2797791, 4990531, 6619374…
$ listing_url <chr> "https://www.airbnb.com/r…
$ scrape_id <dbl> 2.021093e+13, 2.021093e+1…
$ last_scraped <date> 2021-09-29, 2021-09-28, …
$ name <chr> "Beijing Great Wall Escap…
$ description <chr> "A perfect escape only 2 …
$ neighborhood_overview <chr> "Located in a small villa…
$ picture_url <chr> "https://a0.muscache.com/…
$ host_id <dbl> 14311129, 25729513, 34492…
$ host_url <chr> "https://www.airbnb.com/u…
$ host_name <chr> "Andrew", "Joel", "乐林",…
$ host_since <date> 2014-04-15, 2015-01-07, …
$ host_location <chr> "Beijing, Beijing, China"…
$ host_about <chr> "Been living in Beijing f…
$ host_response_time <chr> "within a few hours", "wi…
$ host_response_rate <chr> "100%", "100%", "N/A", "1…
$ host_acceptance_rate <chr> "73%", "99%", "N/A", "100…
$ host_is_superhost <lgl> FALSE, FALSE, FALSE, FALS…
$ host_thumbnail_url <chr> "https://a0.muscache.com/…
$ host_picture_url <chr> "https://a0.muscache.com/…
$ host_neighbourhood <chr> NA, "Shichahai", NA, NA, …
$ host_listings_count <dbl> 1, 10, 2, 1, 3, 1, 5, 5, …
$ host_total_listings_count <dbl> 1, 10, 2, 1, 3, 1, 5, 5, …
$ host_verifications <chr> "['email', 'phone', 'revi…
$ host_has_profile_pic <lgl> TRUE, TRUE, TRUE, TRUE, T…
$ host_identity_verified <lgl> TRUE, TRUE, TRUE, TRUE, T…
$ neighbourhood <chr> "Beijing, China", "Beijin…
$ neighbourhood_cleansed <chr> "怀柔区 / Huairou", "东城…
$ neighbourhood_group_cleansed <lgl> NA, NA, NA, NA, NA, NA, N…
$ latitude <dbl> 40.47329, 39.94193, 40.44…
$ longitude <dbl> 116.5451, 116.3984, 116.0…
$ property_type <chr> "Entire residential home"…
$ room_type <chr> "Entire home/apt", "Entir…
$ accommodates <dbl> 10, 4, 15, 16, 12, 16, 12…
$ bathrooms <lgl> NA, NA, NA, NA, NA, NA, N…
$ bathrooms_text <chr> "1 bath", "1 bath", "4 ba…
$ bedrooms <dbl> 3, 1, 4, 1, 4, 5, 2, 4, 4…
$ beds <dbl> 3, 2, 4, 2, 5, 9, 11, 12,…
$ amenities <chr> "[\"Dishes and silverware…
$ price <chr> "$1,914.00", "$1,610.00",…
$ minimum_nights <dbl> 1, 29, 1, 1, 1, 1, 1, 1, …
$ maximum_nights <dbl> 1125, 365, 1125, 1125, 11…
$ minimum_minimum_nights <dbl> 1, 29, 1, 1, 1, 1, 1, 1, …
$ maximum_minimum_nights <dbl> 1, 29, 1, 1, 1, 1, 1, 1, …
$ minimum_maximum_nights <dbl> 1125, 1125, 1125, 1125, 1…
$ maximum_maximum_nights <dbl> 1125, 1125, 1125, 1125, 1…
$ minimum_nights_avg_ntm <dbl> 1, 29, 1, 1, 1, 1, 1, 1, …
$ maximum_nights_avg_ntm <dbl> 1125, 1125, 1125, 1125, 1…
$ calendar_updated <lgl> NA, NA, NA, NA, NA, NA, N…
$ has_availability <lgl> TRUE, TRUE, TRUE, TRUE, T…
$ availability_30 <dbl> 20, 0, 25, 30, 24, 26, 24…
$ availability_60 <dbl> 50, 0, 29, 60, 27, 56, 54…
$ availability_90 <dbl> 53, 0, 29, 90, 27, 86, 84…
$ availability_365 <dbl> 234, 118, 29, 365, 298, 3…
$ calendar_last_scraped <date> 2021-09-29, 2021-09-28, …
$ number_of_reviews <dbl> 56, 20, 3, 1, 2, 14, 61, …
$ number_of_reviews_ltm <dbl> 3, 0, 0, 0, 1, 3, 15, 5, …
$ number_of_reviews_l30d <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0…
$ first_review <date> 2015-05-04, 2016-12-31, …
$ last_review <date> 2019-05-11, 2020-07-17, …
$ review_scores_rating <dbl> 4.63, 4.68, 5.00, 0.00, 5…
$ review_scores_accuracy <dbl> 4.72, 4.71, 5.00, NA, 5.0…
$ review_scores_cleanliness <dbl> 4.24, 4.82, 4.67, NA, 5.0…
$ review_scores_checkin <dbl> 4.89, 5.00, 5.00, NA, 5.0…
$ review_scores_communication <dbl> 4.92, 4.88, 5.00, NA, 5.0…
$ review_scores_location <dbl> 4.91, 4.88, 5.00, NA, 5.0…
$ review_scores_value <dbl> 4.30, 4.76, 4.33, NA, 5.0…
$ license <lgl> NA, NA, NA, NA, NA, NA, N…
$ instant_bookable <lgl> FALSE, TRUE, FALSE, TRUE,…
$ calculated_host_listings_count <dbl> 1, 10, 1, 1, 3, 1, 1, 2, …
$ calculated_host_listings_count_entire_homes <dbl> 1, 6, 1, 0, 3, 1, 1, 2, 3…
$ calculated_host_listings_count_private_rooms <dbl> 0, 4, 0, 1, 0, 0, 0, 0, 0…
$ calculated_host_listings_count_shared_rooms <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ reviews_per_month <dbl> 0.72, 0.35, 0.11, 0.02, 0…Summary statistics
listings %>%
skim()| Name | Piped data |
| Number of rows | 6296 |
| Number of columns | 74 |
| _______________________ | |
| Column type frequency: | |
| character | 23 |
| Date | 5 |
| logical | 9 |
| numeric | 37 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| listing_url | 0 | 1.00 | 36 | 37 | 0 | 6296 | 0 |
| name | 0 | 1.00 | 1 | 185 | 0 | 6117 | 0 |
| description | 265 | 0.96 | 2 | 1000 | 0 | 5166 | 0 |
| neighborhood_overview | 1139 | 0.82 | 2 | 1000 | 0 | 3790 | 0 |
| picture_url | 0 | 1.00 | 63 | 112 | 0 | 6032 | 0 |
| host_url | 0 | 1.00 | 41 | 43 | 0 | 2665 | 0 |
| host_name | 0 | 1.00 | 1 | 41 | 0 | 2299 | 0 |
| host_location | 4 | 1.00 | 2 | 40 | 0 | 41 | 0 |
| host_about | 3332 | 0.47 | 1 | 4820 | 0 | 1063 | 1 |
| host_response_time | 0 | 1.00 | 3 | 18 | 0 | 5 | 0 |
| host_response_rate | 0 | 1.00 | 2 | 4 | 0 | 37 | 0 |
| host_acceptance_rate | 0 | 1.00 | 2 | 4 | 0 | 46 | 0 |
| host_thumbnail_url | 0 | 1.00 | 55 | 106 | 0 | 2658 | 0 |
| host_picture_url | 0 | 1.00 | 57 | 109 | 0 | 2658 | 0 |
| host_neighbourhood | 5938 | 0.06 | 2 | 25 | 0 | 28 | 0 |
| host_verifications | 0 | 1.00 | 2 | 151 | 0 | 99 | 0 |
| neighbourhood | 1139 | 0.82 | 14 | 34 | 0 | 8 | 0 |
| neighbourhood_cleansed | 0 | 1.00 | 3 | 16 | 0 | 16 | 0 |
| property_type | 0 | 1.00 | 3 | 35 | 0 | 80 | 0 |
| room_type | 0 | 1.00 | 11 | 15 | 0 | 3 | 0 |
| bathrooms_text | 5 | 1.00 | 6 | 17 | 0 | 70 | 0 |
| amenities | 0 | 1.00 | 27 | 1206 | 0 | 5199 | 0 |
| price | 0 | 1.00 | 6 | 10 | 0 | 3029 | 0 |
Variable type: Date
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| last_scraped | 0 | 1.00 | 2021-09-28 | 2021-09-29 | 2021-09-28 | 2 |
| host_since | 0 | 1.00 | 2013-02-06 | 2021-09-19 | 2019-05-21 | 1370 |
| calendar_last_scraped | 0 | 1.00 | 2021-09-28 | 2021-09-29 | 2021-09-28 | 2 |
| first_review | 3203 | 0.49 | 2015-05-04 | 2021-09-28 | 2020-07-18 | 843 |
| last_review | 3203 | 0.49 | 2016-04-04 | 2021-09-28 | 2021-04-30 | 730 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| host_is_superhost | 0 | 1 | 0.24 | FAL: 4811, TRU: 1485 |
| host_has_profile_pic | 0 | 1 | 1.00 | TRU: 6288, FAL: 8 |
| host_identity_verified | 0 | 1 | 1.00 | TRU: 6279, FAL: 17 |
| neighbourhood_group_cleansed | 6296 | 0 | NaN | : |
| bathrooms | 6296 | 0 | NaN | : |
| calendar_updated | 6296 | 0 | NaN | : |
| has_availability | 0 | 1 | 1.00 | TRU: 6296 |
| license | 6296 | 0 | NaN | : |
| instant_bookable | 0 | 1 | 0.65 | TRU: 4111, FAL: 2185 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| id | 0 | 1.00 | 4.068584e+07 | 9018880.17 | 2.797791e+06 | 3.513261e+07 | 4.309471e+07 | 4.883673e+07 | 5.245929e+07 | ▁▁▂▅▇ |
| scrape_id | 0 | 1.00 | 2.021093e+13 | 0.00 | 2.021093e+13 | 2.021093e+13 | 2.021093e+13 | 2.021093e+13 | 2.021093e+13 | ▁▁▇▁▁ |
| host_id | 0 | 1.00 | 2.545746e+08 | 106847611.68 | 4.984459e+06 | 1.828459e+08 | 2.631712e+08 | 3.492971e+08 | 4.236643e+08 | ▂▅▆▇▇ |
| host_listings_count | 0 | 1.00 | 7.470000e+00 | 14.76 | 0.000000e+00 | 1.000000e+00 | 5.000000e+00 | 9.000000e+00 | 2.570000e+02 | ▇▁▁▁▁ |
| host_total_listings_count | 0 | 1.00 | 7.470000e+00 | 14.76 | 0.000000e+00 | 1.000000e+00 | 5.000000e+00 | 9.000000e+00 | 2.570000e+02 | ▇▁▁▁▁ |
| latitude | 0 | 1.00 | 4.031000e+01 | 0.30 | 3.947000e+01 | 4.019000e+01 | 4.041000e+01 | 4.050000e+01 | 4.095000e+01 | ▁▂▂▇▁ |
| longitude | 0 | 1.00 | 1.164300e+02 | 0.47 | 1.154400e+02 | 1.160200e+02 | 1.164200e+02 | 1.167000e+02 | 1.175000e+02 | ▂▆▇▃▃ |
| accommodates | 0 | 1.00 | 7.100000e+00 | 5.11 | 1.000000e+00 | 2.000000e+00 | 5.000000e+00 | 1.200000e+01 | 1.600000e+01 | ▇▂▂▂▃ |
| bedrooms | 61 | 0.99 | 3.060000e+00 | 2.49 | 1.000000e+00 | 1.000000e+00 | 2.000000e+00 | 5.000000e+00 | 2.500000e+01 | ▇▂▁▁▁ |
| beds | 19 | 1.00 | 4.310000e+00 | 4.33 | 0.000000e+00 | 1.000000e+00 | 3.000000e+00 | 6.000000e+00 | 7.100000e+01 | ▇▁▁▁▁ |
| minimum_nights | 0 | 1.00 | 1.380000e+00 | 9.33 | 1.000000e+00 | 1.000000e+00 | 1.000000e+00 | 1.000000e+00 | 3.650000e+02 | ▇▁▁▁▁ |
| maximum_nights | 0 | 1.00 | 8.738100e+02 | 418.94 | 1.000000e+00 | 3.650000e+02 | 1.125000e+03 | 1.125000e+03 | 1.125000e+03 | ▂▂▁▁▇ |
| minimum_minimum_nights | 0 | 1.00 | 1.360000e+00 | 9.30 | 1.000000e+00 | 1.000000e+00 | 1.000000e+00 | 1.000000e+00 | 3.650000e+02 | ▇▁▁▁▁ |
| maximum_minimum_nights | 0 | 1.00 | 1.540000e+00 | 15.67 | 1.000000e+00 | 1.000000e+00 | 1.000000e+00 | 1.000000e+00 | 1.000000e+03 | ▇▁▁▁▁ |
| minimum_maximum_nights | 0 | 1.00 | 9.331500e+02 | 378.79 | 1.000000e+00 | 1.125000e+03 | 1.125000e+03 | 1.125000e+03 | 1.125000e+03 | ▁▁▁▁▇ |
| maximum_maximum_nights | 0 | 1.00 | 9.353200e+02 | 377.05 | 1.000000e+00 | 1.125000e+03 | 1.125000e+03 | 1.125000e+03 | 1.125000e+03 | ▁▁▁▁▇ |
| minimum_nights_avg_ntm | 0 | 1.00 | 1.410000e+00 | 9.98 | 1.000000e+00 | 1.000000e+00 | 1.000000e+00 | 1.000000e+00 | 3.650000e+02 | ▇▁▁▁▁ |
| maximum_nights_avg_ntm | 0 | 1.00 | 9.349400e+02 | 376.98 | 1.000000e+00 | 1.125000e+03 | 1.125000e+03 | 1.125000e+03 | 1.125000e+03 | ▁▁▁▁▇ |
| availability_30 | 0 | 1.00 | 1.917000e+01 | 9.82 | 0.000000e+00 | 1.600000e+01 | 2.400000e+01 | 2.500000e+01 | 3.000000e+01 | ▅▁▁▇▇ |
| availability_60 | 0 | 1.00 | 4.524000e+01 | 16.59 | 0.000000e+00 | 3.500000e+01 | 5.300000e+01 | 5.500000e+01 | 6.000000e+01 | ▁▁▂▁▇ |
| availability_90 | 0 | 1.00 | 7.160000e+01 | 24.32 | 0.000000e+00 | 6.300000e+01 | 8.300000e+01 | 8.500000e+01 | 9.000000e+01 | ▁▁▁▂▇ |
| availability_365 | 0 | 1.00 | 2.493600e+02 | 126.21 | 0.000000e+00 | 1.530000e+02 | 3.370000e+02 | 3.590000e+02 | 3.650000e+02 | ▂▂▂▁▇ |
| number_of_reviews | 0 | 1.00 | 3.300000e+00 | 11.44 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 2.000000e+00 | 4.600000e+02 | ▇▁▁▁▁ |
| number_of_reviews_ltm | 0 | 1.00 | 1.450000e+00 | 4.37 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 1.000000e+00 | 9.700000e+01 | ▇▁▁▁▁ |
| number_of_reviews_l30d | 0 | 1.00 | 1.300000e-01 | 0.64 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 1.600000e+01 | ▇▁▁▁▁ |
| review_scores_rating | 3203 | 0.49 | 4.670000e+00 | 1.00 | 0.000000e+00 | 4.840000e+00 | 5.000000e+00 | 5.000000e+00 | 5.000000e+00 | ▁▁▁▁▇ |
| review_scores_accuracy | 3312 | 0.47 | 4.900000e+00 | 0.37 | 1.000000e+00 | 5.000000e+00 | 5.000000e+00 | 5.000000e+00 | 5.000000e+00 | ▁▁▁▁▇ |
| review_scores_cleanliness | 3312 | 0.47 | 4.870000e+00 | 0.39 | 1.000000e+00 | 4.920000e+00 | 5.000000e+00 | 5.000000e+00 | 5.000000e+00 | ▁▁▁▁▇ |
| review_scores_checkin | 3312 | 0.47 | 4.910000e+00 | 0.36 | 1.000000e+00 | 5.000000e+00 | 5.000000e+00 | 5.000000e+00 | 5.000000e+00 | ▁▁▁▁▇ |
| review_scores_communication | 3312 | 0.47 | 4.920000e+00 | 0.35 | 1.000000e+00 | 5.000000e+00 | 5.000000e+00 | 5.000000e+00 | 5.000000e+00 | ▁▁▁▁▇ |
| review_scores_location | 3312 | 0.47 | 4.860000e+00 | 0.38 | 1.000000e+00 | 4.890000e+00 | 5.000000e+00 | 5.000000e+00 | 5.000000e+00 | ▁▁▁▁▇ |
| review_scores_value | 3312 | 0.47 | 4.800000e+00 | 0.48 | 1.000000e+00 | 4.800000e+00 | 5.000000e+00 | 5.000000e+00 | 5.000000e+00 | ▁▁▁▁▇ |
| calculated_host_listings_count | 0 | 1.00 | 5.910000e+00 | 6.12 | 1.000000e+00 | 1.000000e+00 | 4.000000e+00 | 8.000000e+00 | 3.300000e+01 | ▇▂▁▁▁ |
| calculated_host_listings_count_entire_homes | 0 | 1.00 | 2.350000e+00 | 3.83 | 0.000000e+00 | 0.000000e+00 | 1.000000e+00 | 2.000000e+00 | 3.100000e+01 | ▇▁▁▁▁ |
| calculated_host_listings_count_private_rooms | 0 | 1.00 | 3.490000e+00 | 5.33 | 0.000000e+00 | 0.000000e+00 | 1.000000e+00 | 5.000000e+00 | 3.300000e+01 | ▇▁▁▁▁ |
| calculated_host_listings_count_shared_rooms | 0 | 1.00 | 7.000000e-02 | 0.64 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 9.000000e+00 | ▇▁▁▁▁ |
| reviews_per_month | 3203 | 0.49 | 5.200000e-01 | 0.84 | 2.000000e-02 | 1.100000e-01 | 2.600000e-01 | 6.100000e-01 | 1.600000e+01 | ▇▁▁▁▁ |
Data wrangling
Since price is a quantitative variable, we need to make sure it is stored as numeric data num in the dataframe.
listings <- listings %>%
mutate(price = parse_number(price))typeof(listings$price)[1] "double"skim(listings)| Name | listings |
| Number of rows | 6296 |
| Number of columns | 74 |
| _______________________ | |
| Column type frequency: | |
| character | 22 |
| Date | 5 |
| logical | 9 |
| numeric | 38 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| listing_url | 0 | 1.00 | 36 | 37 | 0 | 6296 | 0 |
| name | 0 | 1.00 | 1 | 185 | 0 | 6117 | 0 |
| description | 265 | 0.96 | 2 | 1000 | 0 | 5166 | 0 |
| neighborhood_overview | 1139 | 0.82 | 2 | 1000 | 0 | 3790 | 0 |
| picture_url | 0 | 1.00 | 63 | 112 | 0 | 6032 | 0 |
| host_url | 0 | 1.00 | 41 | 43 | 0 | 2665 | 0 |
| host_name | 0 | 1.00 | 1 | 41 | 0 | 2299 | 0 |
| host_location | 4 | 1.00 | 2 | 40 | 0 | 41 | 0 |
| host_about | 3332 | 0.47 | 1 | 4820 | 0 | 1063 | 1 |
| host_response_time | 0 | 1.00 | 3 | 18 | 0 | 5 | 0 |
| host_response_rate | 0 | 1.00 | 2 | 4 | 0 | 37 | 0 |
| host_acceptance_rate | 0 | 1.00 | 2 | 4 | 0 | 46 | 0 |
| host_thumbnail_url | 0 | 1.00 | 55 | 106 | 0 | 2658 | 0 |
| host_picture_url | 0 | 1.00 | 57 | 109 | 0 | 2658 | 0 |
| host_neighbourhood | 5938 | 0.06 | 2 | 25 | 0 | 28 | 0 |
| host_verifications | 0 | 1.00 | 2 | 151 | 0 | 99 | 0 |
| neighbourhood | 1139 | 0.82 | 14 | 34 | 0 | 8 | 0 |
| neighbourhood_cleansed | 0 | 1.00 | 3 | 16 | 0 | 16 | 0 |
| property_type | 0 | 1.00 | 3 | 35 | 0 | 80 | 0 |
| room_type | 0 | 1.00 | 11 | 15 | 0 | 3 | 0 |
| bathrooms_text | 5 | 1.00 | 6 | 17 | 0 | 70 | 0 |
| amenities | 0 | 1.00 | 27 | 1206 | 0 | 5199 | 0 |
Variable type: Date
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| last_scraped | 0 | 1.00 | 2021-09-28 | 2021-09-29 | 2021-09-28 | 2 |
| host_since | 0 | 1.00 | 2013-02-06 | 2021-09-19 | 2019-05-21 | 1370 |
| calendar_last_scraped | 0 | 1.00 | 2021-09-28 | 2021-09-29 | 2021-09-28 | 2 |
| first_review | 3203 | 0.49 | 2015-05-04 | 2021-09-28 | 2020-07-18 | 843 |
| last_review | 3203 | 0.49 | 2016-04-04 | 2021-09-28 | 2021-04-30 | 730 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| host_is_superhost | 0 | 1 | 0.24 | FAL: 4811, TRU: 1485 |
| host_has_profile_pic | 0 | 1 | 1.00 | TRU: 6288, FAL: 8 |
| host_identity_verified | 0 | 1 | 1.00 | TRU: 6279, FAL: 17 |
| neighbourhood_group_cleansed | 6296 | 0 | NaN | : |
| bathrooms | 6296 | 0 | NaN | : |
| calendar_updated | 6296 | 0 | NaN | : |
| has_availability | 0 | 1 | 1.00 | TRU: 6296 |
| license | 6296 | 0 | NaN | : |
| instant_bookable | 0 | 1 | 0.65 | TRU: 4111, FAL: 2185 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| id | 0 | 1.00 | 4.068584e+07 | 9018880.17 | 2.797791e+06 | 3.513261e+07 | 4.309471e+07 | 4.883673e+07 | 5.245929e+07 | ▁▁▂▅▇ |
| scrape_id | 0 | 1.00 | 2.021093e+13 | 0.00 | 2.021093e+13 | 2.021093e+13 | 2.021093e+13 | 2.021093e+13 | 2.021093e+13 | ▁▁▇▁▁ |
| host_id | 0 | 1.00 | 2.545746e+08 | 106847611.68 | 4.984459e+06 | 1.828459e+08 | 2.631712e+08 | 3.492971e+08 | 4.236643e+08 | ▂▅▆▇▇ |
| host_listings_count | 0 | 1.00 | 7.470000e+00 | 14.76 | 0.000000e+00 | 1.000000e+00 | 5.000000e+00 | 9.000000e+00 | 2.570000e+02 | ▇▁▁▁▁ |
| host_total_listings_count | 0 | 1.00 | 7.470000e+00 | 14.76 | 0.000000e+00 | 1.000000e+00 | 5.000000e+00 | 9.000000e+00 | 2.570000e+02 | ▇▁▁▁▁ |
| latitude | 0 | 1.00 | 4.031000e+01 | 0.30 | 3.947000e+01 | 4.019000e+01 | 4.041000e+01 | 4.050000e+01 | 4.095000e+01 | ▁▂▂▇▁ |
| longitude | 0 | 1.00 | 1.164300e+02 | 0.47 | 1.154400e+02 | 1.160200e+02 | 1.164200e+02 | 1.167000e+02 | 1.175000e+02 | ▂▆▇▃▃ |
| accommodates | 0 | 1.00 | 7.100000e+00 | 5.11 | 1.000000e+00 | 2.000000e+00 | 5.000000e+00 | 1.200000e+01 | 1.600000e+01 | ▇▂▂▂▃ |
| bedrooms | 61 | 0.99 | 3.060000e+00 | 2.49 | 1.000000e+00 | 1.000000e+00 | 2.000000e+00 | 5.000000e+00 | 2.500000e+01 | ▇▂▁▁▁ |
| beds | 19 | 1.00 | 4.310000e+00 | 4.33 | 0.000000e+00 | 1.000000e+00 | 3.000000e+00 | 6.000000e+00 | 7.100000e+01 | ▇▁▁▁▁ |
| price | 0 | 1.00 | 2.417430e+03 | 2676.84 | 5.900000e+01 | 6.190000e+02 | 1.555000e+03 | 3.511500e+03 | 6.399500e+04 | ▇▁▁▁▁ |
| minimum_nights | 0 | 1.00 | 1.380000e+00 | 9.33 | 1.000000e+00 | 1.000000e+00 | 1.000000e+00 | 1.000000e+00 | 3.650000e+02 | ▇▁▁▁▁ |
| maximum_nights | 0 | 1.00 | 8.738100e+02 | 418.94 | 1.000000e+00 | 3.650000e+02 | 1.125000e+03 | 1.125000e+03 | 1.125000e+03 | ▂▂▁▁▇ |
| minimum_minimum_nights | 0 | 1.00 | 1.360000e+00 | 9.30 | 1.000000e+00 | 1.000000e+00 | 1.000000e+00 | 1.000000e+00 | 3.650000e+02 | ▇▁▁▁▁ |
| maximum_minimum_nights | 0 | 1.00 | 1.540000e+00 | 15.67 | 1.000000e+00 | 1.000000e+00 | 1.000000e+00 | 1.000000e+00 | 1.000000e+03 | ▇▁▁▁▁ |
| minimum_maximum_nights | 0 | 1.00 | 9.331500e+02 | 378.79 | 1.000000e+00 | 1.125000e+03 | 1.125000e+03 | 1.125000e+03 | 1.125000e+03 | ▁▁▁▁▇ |
| maximum_maximum_nights | 0 | 1.00 | 9.353200e+02 | 377.05 | 1.000000e+00 | 1.125000e+03 | 1.125000e+03 | 1.125000e+03 | 1.125000e+03 | ▁▁▁▁▇ |
| minimum_nights_avg_ntm | 0 | 1.00 | 1.410000e+00 | 9.98 | 1.000000e+00 | 1.000000e+00 | 1.000000e+00 | 1.000000e+00 | 3.650000e+02 | ▇▁▁▁▁ |
| maximum_nights_avg_ntm | 0 | 1.00 | 9.349400e+02 | 376.98 | 1.000000e+00 | 1.125000e+03 | 1.125000e+03 | 1.125000e+03 | 1.125000e+03 | ▁▁▁▁▇ |
| availability_30 | 0 | 1.00 | 1.917000e+01 | 9.82 | 0.000000e+00 | 1.600000e+01 | 2.400000e+01 | 2.500000e+01 | 3.000000e+01 | ▅▁▁▇▇ |
| availability_60 | 0 | 1.00 | 4.524000e+01 | 16.59 | 0.000000e+00 | 3.500000e+01 | 5.300000e+01 | 5.500000e+01 | 6.000000e+01 | ▁▁▂▁▇ |
| availability_90 | 0 | 1.00 | 7.160000e+01 | 24.32 | 0.000000e+00 | 6.300000e+01 | 8.300000e+01 | 8.500000e+01 | 9.000000e+01 | ▁▁▁▂▇ |
| availability_365 | 0 | 1.00 | 2.493600e+02 | 126.21 | 0.000000e+00 | 1.530000e+02 | 3.370000e+02 | 3.590000e+02 | 3.650000e+02 | ▂▂▂▁▇ |
| number_of_reviews | 0 | 1.00 | 3.300000e+00 | 11.44 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 2.000000e+00 | 4.600000e+02 | ▇▁▁▁▁ |
| number_of_reviews_ltm | 0 | 1.00 | 1.450000e+00 | 4.37 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 1.000000e+00 | 9.700000e+01 | ▇▁▁▁▁ |
| number_of_reviews_l30d | 0 | 1.00 | 1.300000e-01 | 0.64 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 1.600000e+01 | ▇▁▁▁▁ |
| review_scores_rating | 3203 | 0.49 | 4.670000e+00 | 1.00 | 0.000000e+00 | 4.840000e+00 | 5.000000e+00 | 5.000000e+00 | 5.000000e+00 | ▁▁▁▁▇ |
| review_scores_accuracy | 3312 | 0.47 | 4.900000e+00 | 0.37 | 1.000000e+00 | 5.000000e+00 | 5.000000e+00 | 5.000000e+00 | 5.000000e+00 | ▁▁▁▁▇ |
| review_scores_cleanliness | 3312 | 0.47 | 4.870000e+00 | 0.39 | 1.000000e+00 | 4.920000e+00 | 5.000000e+00 | 5.000000e+00 | 5.000000e+00 | ▁▁▁▁▇ |
| review_scores_checkin | 3312 | 0.47 | 4.910000e+00 | 0.36 | 1.000000e+00 | 5.000000e+00 | 5.000000e+00 | 5.000000e+00 | 5.000000e+00 | ▁▁▁▁▇ |
| review_scores_communication | 3312 | 0.47 | 4.920000e+00 | 0.35 | 1.000000e+00 | 5.000000e+00 | 5.000000e+00 | 5.000000e+00 | 5.000000e+00 | ▁▁▁▁▇ |
| review_scores_location | 3312 | 0.47 | 4.860000e+00 | 0.38 | 1.000000e+00 | 4.890000e+00 | 5.000000e+00 | 5.000000e+00 | 5.000000e+00 | ▁▁▁▁▇ |
| review_scores_value | 3312 | 0.47 | 4.800000e+00 | 0.48 | 1.000000e+00 | 4.800000e+00 | 5.000000e+00 | 5.000000e+00 | 5.000000e+00 | ▁▁▁▁▇ |
| calculated_host_listings_count | 0 | 1.00 | 5.910000e+00 | 6.12 | 1.000000e+00 | 1.000000e+00 | 4.000000e+00 | 8.000000e+00 | 3.300000e+01 | ▇▂▁▁▁ |
| calculated_host_listings_count_entire_homes | 0 | 1.00 | 2.350000e+00 | 3.83 | 0.000000e+00 | 0.000000e+00 | 1.000000e+00 | 2.000000e+00 | 3.100000e+01 | ▇▁▁▁▁ |
| calculated_host_listings_count_private_rooms | 0 | 1.00 | 3.490000e+00 | 5.33 | 0.000000e+00 | 0.000000e+00 | 1.000000e+00 | 5.000000e+00 | 3.300000e+01 | ▇▁▁▁▁ |
| calculated_host_listings_count_shared_rooms | 0 | 1.00 | 7.000000e-02 | 0.64 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 9.000000e+00 | ▇▁▁▁▁ |
| reviews_per_month | 3203 | 0.49 | 5.200000e-01 | 0.84 | 2.000000e-02 | 1.100000e-01 | 2.600000e-01 | 6.100000e-01 | 1.600000e+01 | ▇▁▁▁▁ |
Data visualisations
# Price (per bedroom) distribution by room type
listings %>%
filter (!is.na(room_type)) %>%
mutate ( price_per_bedroom := price/bedrooms ) %>%
ggplot(aes(x=price_per_bedroom, colour = room_type, alpha=0.4)) +
geom_histogram() +
facet_wrap(~room_type, scales = "free")
theme_bw() +
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- attr(*, "validate")= logi TRUElistings %>%
filter (!is.na(neighbourhood_cleansed)) %>%
mutate ( price_per_bedroom := price/bedrooms ) %>%
ggplot(aes( x = factor(neighbourhood_cleansed))) +
geom_boxplot(aes(y = price_per_bedroom)) +
theme( axis.text.x = element_text( angle= 45, hjust = 1)) +
scale_y_continuous(limits = c(0,2500)) +
labs(title = "Box Plot of Price per Bedroom by Neighbourhoods")
listings <- listings %>%
mutate(log_price := log(price)) # Mutate a new column showing log price
ggpairs(listings, columns = c("log_price", "accommodates", "bedrooms", "availability_30", "availability_60", "review_scores_rating", "beds", "number_of_reviews", "minimum_nights"))
listings %>%
filter (!is.na(host_is_superhost)) %>%
mutate ( price_per_bedroom := price/bedrooms ) %>%
ggplot(aes( x = factor(host_is_superhost))) +
geom_boxplot(aes(y = price_per_bedroom)) +
theme( axis.text.x = element_text( angle= 45, hjust = 1)) +
scale_y_continuous(limits = c(0,2500)) +
labs(title = "Box Plot of Price per Bedroom by the host")
Propery types
listings <- listings %>%
mutate(prop_type_simplified = case_when(
property_type %in% c("Entire villa","Entire residential home", "Farm stay","Private room in farm stay") ~ property_type,
TRUE ~ "Other"
))Use the code below to check that prop_type_simplified was correctly made.
listings %>%
count(property_type, prop_type_simplified) %>%
arrange(desc(n)) | property_type | prop_type_simplified | n |
|---|---|---|
| Entire villa | Entire villa | 812 |
| Entire residential home | Entire residential home | 620 |
| Farm stay | Farm stay | 618 |
| Private room in farm stay | Private room in farm stay | 556 |
| Entire cottage | Other | 516 |
| Private room in kezhan | Other | 375 |
| Private room in villa | Other | 290 |
| Room in boutique hotel | Other | 279 |
| Private room in residential home | Other | 273 |
| Room in hotel | Other | 233 |
| Entire bungalow | Other | 217 |
| Private room in cottage | Other | 207 |
| Entire rental unit | Other | 156 |
| Entire townhouse | Other | 135 |
| Private room in bed and breakfast | Other | 118 |
| Private room in serviced apartment | Other | 84 |
| Private room in resort | Other | 80 |
| Private room in bungalow | Other | 68 |
| Entire loft | Other | 61 |
| Kezhan | Other | 60 |
| Private room in nature lodge | Other | 53 |
| Private room in townhouse | Other | 46 |
| Entire cabin | Other | 40 |
| Private room in rental unit | Other | 40 |
| Shared room in hostel | Other | 32 |
| Entire condominium (condo) | Other | 28 |
| Entire serviced apartment | Other | 28 |
| Private room in hostel | Other | 26 |
| Private room | Other | 23 |
| Earth house | Other | 22 |
| Room in aparthotel | Other | 17 |
| Private room in loft | Other | 15 |
| Private room in guesthouse | Other | 14 |
| Entire place | Other | 11 |
| Entire chalet | Other | 10 |
| Campsite | Other | 9 |
| Private room in earth house | Other | 7 |
| Entire bed and breakfast | Other | 6 |
| Entire home/apt | Other | 6 |
| Private room in cabin | Other | 6 |
| Private room in cave | Other | 5 |
| Private room in guest suite | Other | 5 |
| Private room in minsu | Other | 5 |
| Barn | Other | 4 |
| Entire guest suite | Other | 4 |
| Private room in barn | Other | 4 |
| Private room in castle | Other | 4 |
| Ranch | Other | 4 |
| Shared room in bed and breakfast | Other | 4 |
| Shared room in cottage | Other | 4 |
| Shared room in farm stay | Other | 4 |
| Shared room in kezhan | Other | 4 |
| Casa particular | Other | 3 |
| Minsu | Other | 3 |
| Private room in condominium (condo) | Other | 3 |
| Shared room in boutique hotel | Other | 3 |
| Tiny house | Other | 3 |
| Castle | Other | 2 |
| Entire guesthouse | Other | 2 |
| Entire resort | Other | 2 |
| Hut | Other | 2 |
| Private room in hut | Other | 2 |
| Private room in ranch | Other | 2 |
| Private room in tiny house | Other | 2 |
| Private room in treehouse | Other | 2 |
| Shared room in rental unit | Other | 2 |
| Shared room in villa | Other | 2 |
| Cave | Other | 1 |
| Entire hostel | Other | 1 |
| Holiday park | Other | 1 |
| Houseboat | Other | 1 |
| Pension | Other | 1 |
| Private room in camper/rv | Other | 1 |
| Private room in dome house | Other | 1 |
| Private room in ryokan | Other | 1 |
| Private room in shipping container | Other | 1 |
| Riad | Other | 1 |
| Shared room in earth house | Other | 1 |
| Shared room in townhouse | Other | 1 |
| Treehouse | Other | 1 |
Airbnb is most commonly used for travel purposes, i.e., as an alternative to traditional hotels. We only want to include listings in our regression analysis that are intended for travel purposes:
listings %>%
count(minimum_nights)| minimum_nights | n |
|---|---|
| 1 | 6197 |
| 2 | 44 |
| 3 | 8 |
| 4 | 1 |
| 5 | 2 |
| 7 | 5 |
| 10 | 8 |
| 15 | 2 |
| 29 | 7 |
| 30 | 18 |
| 360 | 1 |
| 365 | 3 |
There are some unusual figures for minimum_nights such as, Airbnb does this to encourage customers to stay longer and spend more money.`
listings <- listings %>%
filter(minimum_nights <= 4)Mapping
leaflet(data = filter(listings, minimum_nights <= 4)) %>%
addProviderTiles("OpenStreetMap.Mapnik") %>%
addCircleMarkers(lng = ~longitude,
lat = ~latitude,
radius = 1,
fillColor = "blue",
fillOpacity = 0.4,
popup = ~listing_url,
label = ~property_type)